Method and system for computer-aided decision guidance

ABSTRACT

A system for computer-aided decision guidance includes and/or interfaces with a computing system. A method for computer-aided decision guidance includes: receiving a set of data; determining a set of parameters associated with the set of data; and triggering an output based on the set of parameters. Additionally or alternatively, the method can include analyzing the set of data and/or any other suitable processes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/843,099, filed 17 Jun. 2022, which claims the benefit of U.S.Provisional Application No. 63/212,005, filed 17 Jun. 2021, which isincorporated in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the signals processing anddecision-making fields, and more specifically to a new and useful systemand method for computer-aided care decision guidance in the signalsprocessing and decision-making fields.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic of a system for computer-aided decision guidance.

FIG. 2 is a schematic of a method for computer-aided decision guidance.

FIG. 3 is a schematic variation of a portion of the method forcomputer-aided decision guidance.

FIG. 4 depicts a schematic variation of the system and method forcomputer-aided decision guidance.

FIGS. 5A-5B depict a variation of a set of images along with a set ofparameters used in the method for computer-aided decision guidance.

FIGS. 6A-6E depict a variation of a modeled set of images used in themethod for computer-aided decision guidance.

FIG. 7 depicts a schematic variation of the method for computer-aideddecision guidance.

FIG. 8 depicts a schematic variation of information flow within a systemand method for computer-aided decision guidance.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview

As shown in FIG. 1 , a system 100 for computer-aided decision guidanceincludes and/or interfaces with a computing system. Additionally oralternatively, the system can include and/or interface with anapplication and/or any other components.

Further additionally or alternatively, the system 100 can include and/orinterface with any or all of the systems, components, embodiments,and/or examples described in any or all of: U.S. application Ser. No.16/012,458, filed 19 Jun. 2018, U.S. application Ser. No. 16/012,495,filed 19 Jun. 2018, U.S. application Ser. No. 16/913,754, filed 26 Jun.2020, U.S. application Ser. No. 16/938,598, filed 24 Jul. 2020, U.S.application Ser. No. 17/001,218, filed 24 Aug. 2020, and U.S.application Ser. No. 16/688,721, filed 19 Nov. 2019, and U.S.application Ser. No. 17/385,326, filed 26 Jul. 2021, each of which isincorporated in its entirety by this reference.

As shown in FIG. 2 , a method 200 for computer-aided decision guidanceincludes: receiving a set of data S210; determining a set of parametersassociated with the set of data S230; and triggering an output based onthe set of parameters S230. Additionally or alternatively, the method200 can include analyzing the set of data S220 and/or any other suitableprocesses performed in any suitable order.

Further additionally or alternatively, the method can include any or allof the methods, processes, embodiments, and/or examples as described inU.S. application Ser. No. 16/012,458, filed 19 Jun. 2018, U.S.application Ser. No. 16/012,495, filed 19 Jun. 2018, U.S. applicationSer. No. 16/913,754, filed 26 Jun. 2020, U.S. application Ser. No.16/938,598, filed 24 Jul. 2020, U.S. application Ser. No. 17/001,218,filed 24 Aug. 2020, and U.S. application Ser. No. 16/688,721, filed 19Nov. 2019, and U.S. application Ser. No. 17/385,326, filed 26 Jul. 2021,each of which is incorporated in its entirety by this reference, or anyother suitable processes performed in any suitable order. The method 200can be performed with a system as described above and/or any othersuitable system.

The method 200 can be performed with a system as described above and/orany other suitable system.

2. Benefits

The system and method for computer-aided decision guidance can conferseveral benefits over current systems and methods.

In a first variation, the technology confers the benefit of helpingphysicians make fast and accurate decisions related to the treatment(e.g., surgery, drug administration, etc.) of patients experiencing anacute, time-sensitive condition (e.g., stroke), which can in turnfunction to reduce waste, improve outcomes, and/or otherwise benefit thepatient or users. In specific examples, this is enabled through any orall of: warning surgeons of obstacles that may cause delays during aprocedure (e.g., recommending a point of entry for a catheter,highlighting vascular geometries and properties which may be difficultor impossible to navigate with certain catheters, etc.); preventingsurgeons from having to try multiple devices to successfully perform thesurgery; reducing the waste associated with incorrect device choice;reducing the number of secondary procedures needed to correct for anon-optimal first procedure; and/or perform any other functions.

In a second variation, additional or alternative to the first, thetechnology confers the benefit of providing a mobile platform with whichto prep and/or plan for surgeries or other treatments. In specificexamples, this can enable any or all of: viewing images and/or models ofimages at a client application (e.g., while the surgeon is en route tothe healthcare facility and/or to the patient), prepping for a surgeryearlier than conventionally enabled (e.g., selecting medical devices tobe ready for surgery before reaching the healthcare facility, selectingmedical devices to be ready for surgery before or in parallel withviewing images at a workstation, etc.), establishing communicationbetween multiple care team members and/or between a care team member anda medical technician prepping for the surgery, scheduling a surgeryearlier than conventionally scheduled, and/or can perform any otherfunctions. In a particular specific example, for instance, the systemand method enable 3D modeling of the images to be viewed at a clientapplication executable on mobile devices of the users (e.g., surgeons,care team members, etc.), such that the users can view the 3D models andplan for surgeries in a mobile and/or remote setting relative to thehealthcare facility. Additionally or alternatively, the system andmethod can enable viewing and/or interactions at an augmented reality(AR) system, a virtual reality (VR) system, a mixed reality (MR), otherextended reality (XR) systems, and/or any other systems.

In a third variation, additional or alternative to those describedabove, the technology confers the benefit of automatically producing oneor more outputs related to the treatment of a patient presenting with anacute condition, such as any or all of: making an automaticrecommendation of a device for surgery (e.g., automatically selecting acatheter type or size based on a set of machine learning models);automatically triggering the selection of a device for surgery (e.g.,automatically messaging a surgical technologist to prepare a device forsurgery); automatically triggering a call with a medical device salesrepresentative; automatically messaging a medical device salesrepresentative to confirm a device recommendation; automaticallyscheduling a surgery; automatically assembling a care team for surgery;and/or performing any other actions.

Additionally or alternatively, can be performed in non-acute settings(e.g., to plan for surgeries in the future).

Additionally or alternatively, the system and method can confer anyother benefit.

3. System

As shown in FIG. 1 , a system 100 for computer-aided decision guidanceincludes and/or interfaces with a computing system. Additionally oralternatively, the system can include and/or interface with anapplication and/or any other components.

Further additionally or alternatively, the system 100 can include and/orinterface with any or all of the systems, components, embodiments,and/or examples described in any or all of: U.S. application Ser. No.16/012,458, filed 19 Jun. 2018, U.S. application Ser. No. 16/012,495,filed 19 Jun. 2018, U.S. application Ser. No. 16/913,754, filed 26 Jun.2020, U.S. application Ser. No. 16/938,598, filed 24 Jul. 2020, U.S.application Ser. No. 17/001,218, filed 24 Aug. 2020, and U.S.application Ser. No. 16/688,721, filed 19 Nov. 2019, and U.S.application Ser. No. 17/385,326, filed 26 Jul. 2021, each of which isincorporated in its entirety by this reference.

The system 100 functions to provide a platform with which to quicklydetermine and optionally execute on an optimal treatment plan for apatient (e.g., presenting with an acute condition). Additionally oralternatively, the system 100 can function to: efficiently transmitinformation to one or more users, alert users to important and/orcritical information (e.g., while preventing notification fatigue),establish communication between users, enabling the sharing (e.g.,confidential sharing, HIPAA-compliant sharing, de-identified sharing,etc.) of information between users, form and/or initiate a care team forthe patient, assign the patient to one or more users and/or care teams,trigger one or more other actions (e.g., selection of a medical device,assignment of patient to a clinical trial, transfer of patient toanother point of care, etc.), manage and check in on follow-up for thepatient, and/or can perform any other functions.

Additionally or alternatively, the system 100 can function to process aset of images (e.g., with AI, with machine learning, with deep learning,etc.) in order to determine one or more suspected conditions and/or canperform any other suitable functions.

The system 100 is preferably used to perform any or all of the method200 described below, but can additionally or alternatively be used toperform any other suitable methods.

The system preferably interfaces with one or more points of care (e.g.,1st point of care, 2nd point of care, 3rd point of care, etc.). A pointof care preferably refers to a healthcare facility (e.g., a hospital,clinic, urgent care center, rehabilitation center, etc.), but canadditionally or alternatively refer to a particular physician involvedin the treatment of the patient, a particular procedure assigned to thepatient, a particular device and/or treatment (e.g., medication) to beadministered to the patient, and/or any other location, person, and/oritem involved in the care of the patient.

In a set of variations, for instance, a 1st point of care refers to thehealthcare facility at which a patient presents, typically where thepatient first presents (e.g., in an emergency setting). Conventionally,healthcare facilities include spoke facilities, which are often general(e.g., non-specialist, emergency, etc.) facilities, as well as hub(e.g., specialist) facilities, which can be equipped or better equipped(e.g., in comparison to spoke facilities) for certain procedures (e.g.,mechanical thrombectomy), conditions (e.g., stroke), or patients (e.g.,high risk). Patients typically present at a spoke facility as the 1stpoint of care, but can alternatively present to a hub facility, such aswhen it is evident what condition their symptoms reflect, when they havea prior history of a serious condition, when the condition hasprogressed to a high severity, when a hub facility is closest, randomly,or for any other reason. A healthcare facility can include any or allof: a hospital, clinic, ambulance, doctor's office, imaging center,laboratory, primary stroke center (PSC), comprehensive stroke center(CSC), stroke ready center, interventional ready center, rehabilitationfacility, or any other suitable facility involved in patient care and/ordiagnostic testing.

A patient can be presenting with symptoms of a condition, no symptoms(e.g., presenting for routine testing), or any combination. In use casesin which a patient is presenting with a condition, the condition can beany or all of: an emergency condition (e.g., urgent condition), anon-emergency (e.g., non-urgent) condition (e.g., chronic pain), and/orany other suitable conditions. The condition can be associated with anysuitable body part and/or class of condition, such as, but not limitedto, any or all of: brain conditions (e.g., stroke, aneurysm, braincancer, brain tumor, brain bleeding, traumatic brain injury, cerebraledema, etc.), cardiac conditions (e.g., heart attack, arrhythmia, etc.),pulmonary conditions (e.g., lung disease, pulmonary embolism, asthmaattack, etc.), muscular conditions, bone conditions (e.g., bone cancer,bone breaks and/or fractures, etc.), cancers, tumors, blockages, mentalhealth conditions (e.g., depression, suicidal ideation, bipolardisorder, etc.), and/or any other conditions.

A user herein refers to anyone using the system and/or interfacing withthe method, such as someone having an account at a client application(e.g., as described above), someone in contact with someone having anaccount (e.g., who can be reached by someone having an account), and/orany suitable individual involved in the care and/or consult of apatient. A user can optionally be a healthcare worker, wherein ahealthcare worker refers to any individual or entity associated with ahealthcare facility, such as, but not limited to: a physician, emergencyroom physician (e.g., orders appropriate lab and imaging tests inaccordance with a stroke protocol), radiologist (e.g., on-dutyradiologist, healthcare worker reviewing a completed imaging study,healthcare working authoring a final report, etc.), neuroradiologist,specialist (e.g., neurovascular specialist, vascular neurologist,neuro-interventional specialist, neuro-endovascular specialist,expert/specialist in a procedure such as mechanical thrombectomy,cardiac specialist, pulmonary specialist, oncologist, surgeon, etc.),administrative assistant, healthcare facility employee (e.g., staffemployee), emergency responder (e.g., emergency medical technician), orany other suitable individual. A user can additionally or alternativelybe any or all of: an individual associated with a clinical trial (e.g.,clinical trial coordinator, clinical trial recruiter, principalinvestigator, administrator, etc.), a medical device representative(e.g., who advises on which medical device is suitable for a procedure),and/or any other user.

Any or all of the system can optionally be configured for any or all of:a specific user (e.g., his or her notification preferences, his or herpreferred patient lists, etc.), a group and/or team associated with theuser (e.g., a cardiac team's preferences at a particular healthcarefacility), a healthcare facility (e.g., scheduling information foron-call vs. off-call physicians), and/or any other entities.Additionally or alternatively, any or all of the system can be uniformamong users and/or otherwise configured.

3.1 System—Router 110

The system 100 can optionally include and/or interface with a router 110(e.g., medical routing system, DICOM router, as shown in FIG. 4 , etc.),which functions to receive data (e.g., a dataset) to process (e.g.,during the method 200). The data can optionally include images(equivalently referred to herein as instances and scans) taken at animaging modality (e.g., scanner) and optionally via a computing system(e.g., scanner, workstation, PACS server) associated with a point ofcare. The images can be in the Digital Imaging and Communications inMedicine (DICOM) file format (e.g., generated and transferred betweencomputing system in accordance with a DICOM protocol), and/or in anysuitable format. The images preferably include (e.g., are tagged with)and/or are associated with a set of metadata, but can additionally oralternatively include multiple sets of metadata, no metadata, extracted(e.g., removed) metadata (e.g., for regulatory purposes, HIPAAcompliance, etc.), altered (e.g., encrypted, decrypted, deidentified,anonymized etc.) metadata, or any other suitable metadata, tags,identifiers, or other suitable information. In some variations, themethod 200 includes removing any or all of the metadata prior toproviding the instances at a mobile device.

Additionally or alternatively, the data can include any suitable medicaldata (e.g., diagnostic data, patient data, patient history, patientdemographic information, etc.), such as, but not limited to, PACS data,Health-Level 7 (HL7) data, electronic health record (EHR) data, or anyother suitable data, and to forward the data to a remote computingsystem. Further additionally, or alternatively, the data can includenon-image data, such as any other diagnostic information. In somevariations, for instance, the data includes electrical signals, such aselectrocardiogram (ECG) data, which can be processed. Furtheradditionally or alternatively, the data can include any other signalsand/or other data in any suitable data formats.

The router no can include a virtual entity (e.g., virtual machine,virtual server, etc.), a physical entity (e.g., local server), or anycombination. The router can be local (e.g., at a 1st healthcarefacility, 2nd healthcare facility, etc.) and associated with (e.g.,connected to) any or all of: on-site server associated with any or allof the imaging modality, the healthcare facility's PACS architecture(e.g., server associated with physician workstations), any suitablemedical records databases (e.g., electronic health records [EHR]database, electronic medical records [EMR] database, etc.), and/or anyother suitable local server or DICOM compatible device(s). Additionallyor alternatively, the router can be remote (e.g., locate at a remotefacility, remote server, cloud computing system, etc.), and associatedwith any or all of: a remote server associated with the PACS system, amodality, or another DICOM compatible device such as a DICOM router.

The router 110 preferably operates on (e.g., is integrated into) asystem (e.g., computing system, workstation, server, PACS server,imaging modality, scanner, etc.) at a 1^(st) point of care butadditionally or alternatively, at a 2^(nd) point of care, remote server(e.g., physical, virtual, etc.) associated with one or both of the1^(st) point of care and the 2^(nd) point of care (e.g., PACS server,EHR server, HL7 server), a data storage system (e.g., patient records),or any other suitable system. In some variations, the system that therouter operates on is physical (e.g., physical workstation, imagingmodality, scanner, etc.) but can additionally or alternatively includevirtual components (e.g., virtual server, virtual database, cloudcomputing system, etc.).

The router no is preferably configured to receive data (e.g., instances,images, study, series, etc.) from a data collection device (e.g., an ECGdevice, signals recording device, an imaging modality [e.g., computedtomography scanner, magnetic resonance imaging scanner, ultrasoundmachine, etc.], etc.) at a point of care (e.g., spoke, hub, etc.) butcan additionally or alternatively receive data from a second point ofcare (e.g., hub, spoke, etc.), multiple points of care, any otherhealthcare facility, a location other than a healthcare facility (e.g.,ambulance, patient's home, etc.). The router can be coupled in anysuitable way (e.g., wired connection, wireless connection, etc.) to thedata collection device (e.g., directly connected, indirectly connectedvia a PACS server, etc.). Additionally or alternatively, the router canbe connected to the healthcare facility's PACS architecture and/or otherserver or database. The router 110 can additionally or alternativelyreceive any other inputs (e.g., as described below), such as inputs fromclient applications executing on mobile user devices. Alternatively, anyor all of these set of inputs can be otherwise ultimately received(e.g., directly) at a computing system.

In some variations, the router includes a virtual machine operating on acomputing system (e.g., computer, workstation, user device, etc.),imaging modality (e.g., scanner), server (e.g., PACS server, server at1^(st) healthcare facility, server at 2^(nd) healthcare facility, etc.),or other system. In a specific example, the router is part of a virtualmachine server. In another specific example, the router is part of alocal server.

3.2 System—Computing system 120

The system 100 can optionally include and/or interface with a computingand/or processing system 120, which functions to perform any or all of:receiving and processing data packets (e.g., dataset from router),interfacing with a user device (e.g., mobile device), removing metadatafrom a data packet (e.g., to comply with a regulatory agency),determining a set of notifications and/or alerts to send to users,triggering the set of notifications and/or alerts, establishingcommunication between multiple client applications (e.g., as shown inFIG. 3 ), and/or can perform any other suitable function(s).

The computing system and/or processing system can include a remotecomputing and/or processing system (e.g., cloud-based computing system),a local computing system (e.g., at a local server, onboard a mobiledevice or other device, etc.), or any combination.

In preferred variations, at least a portion of the method 200 isperformed at a remote computing system (e.g., cloud-based), butadditionally or alternatively any or all of the method 200 can beperformed at a local computing system.

In some variations, the computing and/or processing system 120 providesan interface for technical support (e.g., for a client application)and/or analytics. Additionally or alternatively, the computing systemcan include storage configured to store and/or access a lookup table,wherein the lookup table functions to determine a treatment option(e.g., particular device), a user to automatically contact, a set ofusers to establish communication between, and/or any other suitableinformation. Additionally or alternatively, any or all of theinformation can be determined with artificial intelligence (AI), such asa with any or all of: a set of machine learning models and/oralgorithms, a set of deep learning models and/or algorithms (e.g.,neural networks, convolutional neural networks, etc.), a set ofmappings, a decision tree, and/or with any other tools.

In some variations, the computing and/or processing system 120 connectsmultiple healthcare facilities and/or users (e.g., through a clientapplication, through a messaging platform, etc.).

In some variations, the computing and/or processing system 120 functionsto receive one or more inputs and/or to monitor a set of applications(e.g., executing on user devices, executing on workstations, etc.).

Additionally or alternatively, the computing and/or processing systemcan perform any other functions.

3.3 System—Application 130

The system 100 preferably includes and/or interfaces with one or moreapplications 130 (e.g., clients, client applications, client applicationexecuting on a device, etc.), which individually or collectivelyfunction to provide one or more outputs (e.g., from a remote computingsystem) to a user. Additionally or alternatively, the applications canindividually or collectively function to receive one or more inputs froma user, provide one or more outputs to a healthcare facility (e.g.,first point of care, second point of care, etc.) and/or a databaseassociated with the healthcare facility (e.g., EMR, EHR, PACS, etc.),establish communication between users, send alerts and/or notificationsto users, and/or perform any other suitable function.

As described above, the application can be partially or fully customizedto users, groups, healthcare facilities, and/or any other entities. Inpreferred variations, for instance, the alerts and notifications can beconfigured based on any or all of: the user's schedule (e.g., on-callvs. not on-call), preferences (e.g., for notification frequency, alerttriggering, etc.), and/or any other information.

The application is preferably configured to be executed on a userdevice, and further preferably a mobile user device (e.g., with any orall of the processing performed at a remote computing system such as acloud-based computing system, with any or all of the processingperformed at the mobile device, any combination, etc.) of the user, suchas a phone, tablet, smart watch, laptop, personal computer, and/or anyother user device. The user device can be personal user device of theuser, a device owned by the healthcare facility, and/or any otherdevice. The application can additionally or alternatively be configuredto execute on any other devices, such as a workstation of the healthcarefacility and/or any other devices. In specific examples, for instance,an application is executed on a mobile device with which the user caninteract (e.g., for viewing images and/or reconstructions, formanipulating images and/or reconstructions, for communicating with otherusers, for receiving user inputs, etc.), wherein processing associatedwith the application is preferably performed at least partially at acloud-based computing system. Additionally or alternatively, any or allof the processing can be performed at the mobile device, at a localserver, at a data collection device, at any combination of devices,and/or at any other locations.

In some variations, one or more features of the application (e.g.,appearance, information content, information displayed, user interface,graphical user interface, etc.) are determined based on any or all of:the type of device that the application is operating on (e.g., userdevice vs. healthcare facility device, mobile device vs. stationarydevice), where the device is located (e.g., 1^(st) point of care, 2^(nd)point of care, etc.), who is interacting with the application (e.g.,user identifier, user security clearance, user permission, etc.), or anyother characteristic. In some variations, for instance, an applicationexecuting on a healthcare facility device will display a 1^(st) set ofinformation (e.g., uncompressed images, metadata, etc.) while anapplication executing on a mobile user device will display a 2^(nd) setof information (e.g., compressed images, no metadata, etc.). In somevariations, the type of data to display is determined based on any orall of: an application identifier, mobile device identifier, workstationidentifier, or any other suitable identifier.

The application is preferably in communication with the computingsystem, but can additionally or alternatively be in communication with arouter and/or any other suitable system components. The applicationpreferably includes and/or interfaces with both front-end (e.g.,application executing on a user device, application executing on aworkstation, etc.) and back-end components (e.g., software, processingat a remote computing system, etc.), but can additionally oralternatively include just front-end or back-end components, or anynumber of components implemented at any suitable system(s).

The outputs provided by the application can include any or all of: analert or notification (e.g., push notification, text message, call,email, etc.); an image set (e.g., compressed version of images taken atscanner, preview of images taken at scanner, images taken at scanner,etc.); a modeled set of images (e.g., as produced in S220); a set oftools for interacting with the image set, such as any or all of panning,zooming, rotating, adjusting window level and width, scrolling,performing maximum intensity projection [MIP] (e.g., option to selectthe slab thickness of a MIP), changing the orientation of a 3D scan(e.g., changing between axial, coronal, and sagittal views, freestyleorientation change), showing multiple views of a set of images; aworklist (e.g., list of patients presenting for and/or requiring care,patients being taken care of by specialist, patients recommended tospecialist, procedures to be performed by specialist, etc.); a set ofpatient lists (e.g., as described below); a messaging platform (e.g.,HIPAA-compliant messaging platform, texting platform, video messaging,group messaging etc.); a telecommunication platform (e.g., videoconferencing platform); a directory of contact information (e.g., 1^(st)point of care contact info, 2^(nd) point of care contact info, etc.);tracking of a workflow or activity (e.g., real-time or near real-timeupdates of patient status/workflow/etc.); analytics based on or relatedto the tracking (e.g., predictive analytics such as predicted timeremaining in radiology workflow or predicted time until stroke reaches acertain severity; average time in a workflow; average time to transitionto a second point of care, etc.); resources and/or content (e.g.,digital handbooks with device specifications and/or instructions forreference); or any other suitable output.

The inputs received at the application can include any or all of theoutputs described previously, touch inputs (e.g., received at atouch-sensitive surface), audio inputs, optical inputs, or any othersuitable input. The set of inputs preferably includes an inputindicating receipt of an output by a recipient (e.g., read receipt of aspecialist upon opening a notification). This can include an activeinput from the user (e.g., contact user selection at application), apassive input (e.g., read receipt), or any other input.

In some variations, the application at least partially functions as amobile PACS viewer, which enables user to view the images and any or allother information associated with the patient and included in PACS.Additionally or alternatively, the application can include any otherinformation (e.g., non-PACS patient information, other user information,healthcare facility information, etc.), a server other than PACS can beintegrated, and/or the application can have any other functions.

The application preferably includes and/or interfaces with acommunication platform including a messaging platform, which functionsto enable communication between multiple users and/or between users andentities (e.g., databases, healthcare facility administrators, technicalsupport, etc.). The messaging platform is preferably a secure platformconfigured to be compliant with healthcare regulations (e.g., HealthInsurance Portability and Accountability Act [HIPAA]) and/or any otherprivacy and/or data security protocols (e.g., encryption protocols).

The messaging platform preferably enables messages (equivalentlyreferred to herein as chats) to be exchanged between users. Thecommunication platform can additionally or alternatively include voicecommunications (e.g., with a Voice over Internet Protocol [VoIP]), whichcan function in some cases to still enable communication even when auser loses connection; video communications (e.g., teleconferencing,video consultations, video communications with a sales representativefor advice during a procedure, etc.); and/or any other communications.The messaging platform is preferably part of the application, but canadditionally or alternatively be a 3rd party application incommunication with the application, a native application to the mobiledevice (e.g., text messaging application), and/or any other application.

In one variation, the system 100 includes a mobile device application130 and a workstation application 130—both in communication with thecomputing system—wherein a shared user identifier (e.g., specialistaccount, user account, etc.) can be used to connect the applications(e.g., retrieve a case, image set, etc.) and determine the informationto be displayed at each application (e.g., variations of imagedatasets). In one example, the information to be displayed (e.g.,compressed images, high-resolution images, etc.) can be determined basedon: the system type (e.g., mobile device, workstation), the applicationtype (e.g., mobile device application, workstation application), theuser account (e.g., permissions, etc.), any other suitable information,or otherwise determined.

The application can include and/or interface with any suitablealgorithms or models (e.g., AI models, machine learning models, deeplearning models, etc.) for analysis (e.g., at a computing and/orprocessing system, retrieved from storage, retrieved from remotestorage, etc.), and part or all of the method 200 can be performed by aprocessor associated with the application. The algorithms and/or modelscan include AI models and/or algorithms, non-AI models and/or algorithms(e.g., programmed models), or any combination. In some variations, forinstance, a set of AI models is used to process the set of images inorder to determine a suspected condition, such as described in any orall of: U.S. application Ser. No. 16/012,458, filed 19 Jun. 2018, U.S.application Ser. No. 16/012,495, filed 19 Jun. 2018, U.S. applicationSer. No. 16/913,754, filed 26 Jun. 2020, U.S. application Ser. No.16/938,598, filed 24 Jul. 2020, U.S. application Ser. No. 17/001,218,filed 24 Aug. 2020, U.S. application Ser. No. 16/688,721, filed 19 Nov.2019, and U.S. application Ser. No. 17/385,326, filed 26 Jul. 2021, eachof which is incorporated in its entirety by this reference. One or moreAI models and/or algorithms can additionally or alternatively functionto implement any or all of the processes described below, such asdetermining which users to establish communication between (e.g., basedon a prediction of which treatment group a patient will require based ona suspected condition), determining a care team for the patient,selecting a procedure and/or medical device for the patient, and/or anyother processes.

Additionally or alternatively, the application can be configured for anyor all of: case sharing, actionable alerts and notifications sent tousers, integrations with 3rd party applications and/or systems, and/orany other actions.

3.4 System—Additional Components

The system 100 and/or or any component of the system 100 can optionallyinclude or be coupled to any suitable component for operation, such as,but not limited to: a processing module (e.g., processor,microprocessor, etc.), control module (e.g., controller,microcontroller), power module (e.g., power source, battery,rechargeable battery, mains power, inductive charger, etc.), sensorsystem (e.g., optical sensor, camera, microphone, motion sensor,location sensor, etc.), or any other suitable components.

4. Method

As shown in FIG. 2 , a method 200 for computer-aided decision guidanceincludes: receiving a set of data S210; determining a set of parametersassociated with the set of data S230; and triggering an output based onthe set of parameters S230. Additionally or alternatively, the method200 can include analyzing the set of data S220 and/or any other suitableprocesses performed in any suitable order.

Further additionally or alternatively, the method can include any or allof the methods, processes, embodiments, and/or examples as described inU.S. application Ser. No. 16/012,458, filed 19 Jun. 2018, U.S.application Ser. No. 16/012,495, filed 19 Jun. 2018, U.S. applicationSer. No. 16/913,754, filed 26 Jun. 2020, U.S. application Ser. No.16/938,598, filed 24 Jul. 2020, U.S. application Ser. No. 17/001,218,filed 24 Aug. 2020, and U.S. application Ser. No. 16/688,721, filed 19Nov. 2019, and U.S. application Ser. No. 17/385,326, filed 26 Jul. 2021,each of which is incorporated in its entirety by this reference, or anyother suitable processes performed in any suitable order. The method 200can be performed with a system as described above and/or any othersuitable system.

The method 200 can be performed with a system 100 as described aboveand/or with any other suitable system.

The method 200 preferably functions to assist physicians in preparingand/or planning for care (e.g., surgical treatment, pharmaceuticaltreatment, long-term care planning, etc.) of a patient, such asproviding information and/or making recommendations related to any orall of: an optimal set of devices with which to perform a surgery, anoptimal path to take during a surgical procedure (e.g., optimalvasculature path and/or point of entry), an optimal surgical team toassemble, a selection of medication for the patient, a selection ofmedication versus surgical treatment for the patient, a determination ofwhether or not to intervene, a determination of when to intervene,and/or can provide any other information and/or recommendations.Additionally or alternatively, the method 200 can function to provideone or more mobile tools (e.g., 3D viewers, messaging platforms, etc.)with which physicians (e.g., surgeons) and/or other care team members(e.g., surgical technologists, nurses, etc.) can interact and/orcommunicate. Further additionally or alternatively, the method 200 canperform any other function(s).

In preferred variations, the method 200 is used in cases of patientspresenting with acute and/or otherwise time-sensitive conditions, suchas cases of stroke (e.g., ischemic stroke, hemorrhagic stroke, etc.).Additionally or alternatively, the method 200 can be implemented in anyother acute cases (e.g., cardiac events, trauma, emergency events,etc.), other brain conditions (e.g., aneurysms), and/or in any otherhealth events associated with a patient.

4.1 Method—Receiving a Set of Data S210

The method 200 can include receiving a set of data S210, which functionsto receive information with which to perform any or all of the remainingprocesses of the method 200. Additionally or alternatively, S210 canfunction to trigger any or all of the processes described below, and/orS210 can perform any other functions.

S210 is preferably performed initially in the method 200 and optionallyat multiple times during the method 200 (e.g., as incoming informationis received, in response to a user request, in response to a useraction, continuously, at a predetermined frequency, at random intervals,at different times for different types of data, etc.). Additionally oralternatively, S210 can be performed at any other times and/or themethod 200 can be performed in absence of S210.

The set of data can include image data, non-image data (e.g., electricalsignals, ECG/EKG signals, demographic information, historicalinformation, etc.), any other data, and/or any combination of data.

In a first set of variations (e.g., involving stroke patients, involvingpatients experiencing a neurological condition, involving patientsexperiencing a cardiological condition, involving patients experiencinga lung pathology, involving patients experiencing trauma, etc.), the setof data includes a set of images, such as images taken at (e.g., sampledat, imaged by, etc.) an imaging modality (e.g., computed tomography [CT]scanner, magnetic resonance imaging [MRI] scanner, ultrasound scanner,etc.). Additionally, the set of data can further include non-image data(e.g., set of signals, demographic information, etc.) and/or any otherdata or combination of data.

In a second set of variations (e.g., involving patients experiencing acardiological condition, involving patients experiencing a lungpathology, involving patients experiencing trauma, involving strokepatients, involving patients experiencing a neurological condition,etc.), the set of data includes non-image data (e.g., ECG/EKG signals).Additionally, the set of data can further include image data and/or anyother data or combination of data.

The set of data is preferably received at a computing system from any orall of: a router, a set of applications (e.g., at multiple userdevices), another computing system and/or database, and/or any othersources. Alternatively, the set of data can be received at any otherlocations from any suitable sources.

In variations in which the set of data includes a set of images, the setof images are preferably received from an imaging modality (e.g.,scanner, CT scanner, MRI scanner, ultrasound imaging device, etc.), PACSor other server, a database (e.g., for historical patient images),and/or from any other sources. The imaging modalities can include, forinstance, any or all of: x-ray, computed tomography (CT) (e.g.,CT-angiography, ECG-gated CT angiography, etc.), magnetic resonanceimaging (MRI), ultrasound, and/or any other modalities. In preferredvariations (e.g., stroke), the set of images show a brain and/or a brainregion of the user, but can additionally or alternatively be associatedwith any other anatomical regions. In additional or alternativevariations, for instance, the set of images correspond to (e.g., depict)a cardiac region (e.g., heart, heart chambers, heart valves, cardiovasculature, etc.) of the user, a lung region of the user, an anatomicalregion experiencing trauma (e.g., broken or fractured bone, puncturedlung, etc.) and/or any other region or combination of regions.

Optionally, any or all of the system and/or method can be optimized forone or more specific modalities. Additionally or alternatively, imagedata can be generated from a camera, user device, accessed from adatabase or web-based platform, drawn, sketched, or otherwise obtained.In a specific example, for instance, the image viewing tools arecustomized based on (e.g., optimized for) the particular imagingmodality (e.g., X-ray vs. CT vs. MRI vs. ultrasound, etc.) associatedwith the set of images, such as any or all of the image manipulationtools. The images are preferably organized into studies, wherein theuser can view a current study and further preferably can view any paststudies. Additionally or alternatively, the user can be associated withany other viewing permissions and/or can view images organized in anyother ways.

The set of inputs can additionally or alternatively include any otherinputs, such as other patient information (e.g., medical history and/ormedical records, preferences, demographic information, lifestyleinformation, etc.), healthcare facility information (e.g., specialties,departments, number of beds available, scheduling information, etc.),specialist information (e.g., preferences, specialty, procedures thespecialist is qualified and/or certified to perform, procedures thespecialist prefers performing and/or is most qualified performing,on-call schedule, etc.), device information (e.g., device handbooks,device specifications, device parameters such as size parameters, deviceinventory at a particular healthcare facility, etc.), devicerepresentative information (e.g., contact information, availability,location, etc.), and/or any other suitable information.

In some variations, for instance, S210 includes retrieving a set ofinputs, such as retrieving historical information (e.g., prior imagingstudies), demographic information, medical information (e.g., frommedical records), and/or any other information associated with a patient(e.g., in response to receiving new image study for the patient).

The set of inputs can further additionally or alternatively include anyinputs received from a user (e.g., specialist, device representative,etc.) at the application (e.g., as described above, as described below,etc.), inputs received from a database (e.g., EMR, EHR, etc.), and/orany other inputs.

In a preferred set of variations, S210 includes receiving a set ofimages for a patient from a scanner (e.g., from a router coupled to ascanner), wherein the set of images is received at a computing system(e.g., remote computing system, local computing system, etc.) forprocessing.

In another set of variations, S210 includes receiving a set of signals(e.g., ECG signals, heart rate signals, etc.) from a signal collectiondevice (e.g., ECG signal collection device, heart rate monitor, bloodpressure cuff, vital signs monitor, etc.).

Additionally or alternatively, S210 can include any other processesand/or be otherwise suitably performed.

4.2 Method—Analyzing the Set of Data S220

The method 200 can include analyzing the set of data S220, whichpreferably functions to enable the determination of a set of parametersassociated with the set of data (e.g., in S230). Additionally oralternatively, analyzing the set of data can function to produce one ormore visualizations with which a user can more clearly assess the imagesand/or plan for a surgery or other treatment. Further additionally oralternatively, S220 can perform any other functions.

S220 is preferably performed in response to and based on S210, andoptionally at multiple times during the method. Additionally oralternatively, S220 can be performed at any other times and/or themethod 200 can be performed in absence of S220. Further additionally oralternatively, S220 can include and/or be performed in response todetecting a suspected condition associated with the set of data (e.g.,as shown in a detected suspected LVO in a set of images as shown inFIGS. 5A-5B). In a first set of variations, for instance, S220 isperformed in response to detecting an acute condition, such as, but notlimited to, any or all of: a brain event (e.g., an ischemic stroke suchas a large vessel occlusion [LVO], a hemorrhagic stroke, etc.), arespiratory event (e.g., pulmonary embolism), a cardiac event (e.g.,heart attack), and/or any other event. The suspected condition ispreferably determined automatically, such as a with a set of trainedmodels (e.g., machine learning models, deep learning models, etc.), butcan additionally or alternatively be determined manually (e.g., by aradiologist), any combination, and/or otherwise determined.

In specific examples, for instance, S220 is performed in response todetecting a suspected condition as described in any or all of: U.S.application Ser. No. 16/012,458, filed 19 Jun. 2018, U.S. applicationSer. No. 16/012,495, filed 19 Jun. 2018, U.S. application Ser. No.16/913,754, filed 26 Jun. 2020, U.S. application Ser. No. 16/938,598,filed 24 Jul. 2020, U.S. application Ser. No. 17/001,218, filed 24 Aug.2020, U.S. application Ser. No. 16/688,721, filed 19 Nov. 2019, and U.S.application Ser. No. 17/385,326, filed 26 Jul. 2021, each of which isincorporated herein in its entirety by this reference.

S220 can be performed with any or all of: a set of models and/oralgorithms (e.g., trained models, machine learning models, deep learningmodels, etc.), a set of rule-based processes, a set of segmentationprocesses (e.g., with segmentation software, with 3rd party software,etc.), a set of decision trees and/or lookup tables, and/or any otherprocesses.

S220 is preferably performed within a predetermined time period, whereinperforming S220 within the predetermined time period is configured toenable the user to plan for care (e.g., surgery) of the patient withoutsignificantly delaying his or her treatment. This time period can be anyor all of: less than the time conventionally required to model a set ofdiagnostic images, less than a threshold time period (e.g., 1 minute, 30seconds, 10 seconds, 5 seconds, less than 5 minutes, between 0 secondsand 2 minutes, less than 10 minutes, etc.), and/or any other timeperiod. Additionally or alternatively, S220 can be performed inaccordance with any other features or parameters.

In variations in which the set of data includes a set of images, S220preferably includes modeling the set of images. Modeling the set ofimages preferably includes determining a three-dimensional (3D)representation (e.g., 3D visualization, 3D reconstruction, etc.) basedon a set of two-dimensional (2D) images received in S210 (e.g., as shownin FIGS. 6A-6E, etc.). The 3D representation is preferably performedwith a set of one or more segmentation processes, but can additionallyor alternatively be performed with any other processes.

Additionally or alternatively, S220 can include annotating any or all ofthe set of images (e.g., to efficiently indicate particular regions to auser, to convey measurements and/or parameters associated with asuspected pathological condition and/or anatomical region, to indicate apotential and/or recommended surgical pathway, etc.) and/or any otherprocesses.

In variations in which the set of data includes non-image data, such asa set of signals, S220 can include a set of signal analysis processes.At least a portion of the signal analysis processes is preferablyperformed with a set of trained models and/or algorithms, but canadditionally or alternatively be performed with a set of rule-basedmodels and/or algorithms, manual processes, and/or any other tools orprocesses.

S220 can optionally include presenting the modeled set of images and/orany other intermediate outputs associated with the set of data to auser, wherein a user preferably refers to a physician (e.g., surgeon,primary care physician, emergency doctor, neuro interventionalist, etc.)and/or any other care team members (e.g., nurse, surgical technologist,etc.) involved in the care of the patient. Additionally oralternatively, the users can include any or all of: medical device salesrepresentatives (e.g., involved in the selection and/or recommendationof a medical device for surgery), clinical trial representative (e.g.,involved in the recruitment of patients for a clinical trial), and/orany other individuals involved in the care and/or planning of care forthe patient.

In some examples, for instance, S220 includes presenting a 3Dvisualization associated with the set of images received in S210 tousers (equivalently referred to herein as a recipients) at a clientapplication executing on a mobile device associated with the user,wherein the 3D visualization can optionally be manipulatable (e.g.,rotatable, scalable, etc.) and/or otherwise interacted with by the user.Additionally or alternatively, the 3D visualization can be viewable atother devices (e.g., a workstation at the healthcare facility), a 2Dvisualization (e.g., one or more 2D images which depict evidence of thesuspected pathology, a single image which depicts a most severe view ofthe suspected pathology such as an image which depicts a largestdiameter of a large vessel occlusion, etc.), and/or otherwise presentedto the user.

Additionally or alternatively, S220 can include any other processes.

4.3 Method—Determining a Set of Parameters Associated with the Set ofData S230

The method 200 can include determining a set of parameters associatedwith the set of data S230, which functions to determine informationwhich care providers (e.g., specialists) can use in performingdecision-making for care of the patient. This can enable, for instance,any or all of: the selection of an optimal medical device to be used insurgery, the selection of an optimal (e.g., most efficacious) drug, theselection of an optimal type of surgery, the determination of an optimalpath and/or entry point for a surgical intervention, and/or can enableany other outcomes.

S230 is preferably performed in response to and based on S220, andoptionally at multiple times during the method. Additionally oralternatively, S230 can be performed during S220 and/or in parallel withS220, at any other times during the method 200, in absence of S220,and/or the method 200 can be performed in absence of S230.

The set of parameters is preferably determined based on one or moreoutcomes (e.g., modeled set of images, processed set of signals, etc.)produced in S220, but can additionally or alternatively be determined inabsence of S220, and/or based on any other information.

The set of parameters is preferably at least partially determinedautomatically, such as with a set of models (e.g., trained models,machine learning models, deep learning models, etc.) and/or algorithms(e.g., as utilized in S220), but can additionally or alternatively bedetermined based on a set of manual processes, and/or with anycombination of processes.

The set of parameters preferably includes one or more geometric featuresassociated with the set of images, such as any or all of: dimensions(e.g., lengths, diameters, curvatures, radii, etc.), volumes, surfaceareas, and/or any other geometric features associated with theanatomical region(s) associated with the set of images.

The parameters can be associated with (e.g., characterize, define, etc.)any or all of: a pathological region and/or feature (e.g., clot size,aneurysm size, fracture location, etc.); a non-pathological regionand/or feature (e.g., vessel diameter proximal to a detected occlusion,vessel diameter of a vessel needed to access an occlusion and/oraneurysm, etc.); any other regions or features; and/or any combinationof regions or features.

In variations involving vasculature, such as vessels in the brain, theset of parameters can include, for instance, one or more vesseldiameters, such as any or all of: a vessel diameter immediately before(e.g., proximal and adjacent to) an occlusion or other landmark (e.g.,along a path that the surgeon would take with a catheter); a diameter ofthe narrowest part of a vessel needed to reach the occlusion or otherlandmark; a total length of the vessels needed to reach the occlusion orother landmark; one or more parameters associated with the tortuosity ofthe vessels (e.g., sharpest angle along a proposed path for reaching anocclusion, average tortuosity of the vessel(s), etc.); and/or any otherparameters. Additionally or alternatively, any other features associatedwith vasculature can be detected, such as vessel calcification (e.g.,presence of calcification, amount of calcification, location ofcalcification, severity of calcification, etc.) and/or any otherfeatures.

In specific examples involving a vessel occlusion (e.g., LVO), the setof parameters determined in S230 can include any or all of: a vesseldiameter immediately before an occlusion based on a proposed vessel pathto access the occlusion (e.g., with a catheter); diameters of thenarrowest part(s) of the vessel(s) along the path and/or the diameter(s)associated with any major arteries such as the internal carotid artery[ICA], middle cerebral artery [MCA] (e.g., M1 segment of MCA, M2 segmentof MCA, M3 segment of MCA, M4 segment of MCA, etc.), anterior cerebralartery [ACA], and/or any other arteries; optionally a total distance ofthe path (e.g., based on aggregating the vessel lengths); a tortuosityof the vessels; and/or any other parameters.

The set of parameters can additionally or alternatively include featuresassociated with the suspected condition. For variations involving anocclusion such as a clot, the set of parameters can include, forinstance, any or all of: a type and/or composition of a clot (e.g.,white clot vs. red clot, a calcified clot, a fibrin-rich vs. alow-fibrin clot, a porosity of a clot, perviousness of a clot,permeability of a clot, etc.); one or more dimensions of a clot (e.g.,diameter, length, largest dimension, volume, surface area, etc.);arrangement of a clot within a vessel (e.g., arranged in a straightportion of the vessel, arranged in a curve of the vessel, etc.); and/orany other features of the clot(s). Any or all of these parameters canoptionally be determined based on intensity values (e.g., HounsfieldUnit [HU] values) associated with the set of images and/or any otherinformation.

In a first set of variations involving a vessel occlusion (e.g., asshown in FIG. 7 ), S230 includes determining any or all of: a set ofvessel diameter values (e.g., smallest diameter values), a set of clotparameters (e.g., white vs. red blood clot, calcification level of clotporosity of clot, etc.), and optionally any or all of a set of vessellengths, other vessel parameters (e.g., tortuosity, maximum curvature,regions having a curvature above a predetermined threshold, diameter,etc.), and/or any other parameters.

In a set of specific examples (e.g., as shown in FIGS. 5A-5B), S230includes determining at least a set of radii associated with a segmentedvessel region, wherein the segmented vessel region is arrangedimmediately before the occlusion.

In another set of specific examples (e.g., as shown in FIGS. 6A-6E),S230 includes determining a diameter of the vessel immediately beforethe collusion; a length from an aorta to the occlusion; and optionallyany other parameters.

Additional variations and types of parameters determined can include anyor all of those described below.

Additionally or alternatively, S230 can include any other processes.

4.4 Method—Producing and/or Triggering an Output Based on the Set ofParameters S240

The method 200 can include producing and/or triggering an output basedon the set of parameters S240, which functions to initiate and/orperform an action related to care of the patient. Additionally oralternatively, S240 can function to: initiate an action in less than apredetermined threshold of time; initiate an action with no and/orminimal user input; prevent the need to try multiple devices and/ortreatment options for a patient; improve patient outcomes (e.g., byselecting an optimal device for surgery); and/or perform any otherfunctions.

S240 is preferably performed in response to and based on S230, andoptionally at multiple times during the method. Additionally oralternatively, S240 can be performed at any other times and/or themethod 200 can be performed in absence of S240.

S240 is preferably performed with a computing system (e.g., as describedabove), further preferably with a set of one or more models (e.g.,machine learning models) and/or algorithms, but can additionally oralternatively be performed with one or more databases, lookup tables,decision trees, and/or any other tools.

In preferred variations, at least a portion of the outputs aredetermined automatically, such as by a computing system (e.g., asdescribed above). Additionally or alternatively, any or all of theoutputs can be determined manually, partially automatically (e.g.,automatically with user input), and/or any combination.

S240 can optionally include selecting (e.g., recommending, initiating,etc.) a type of procedure and/or other treatment option for the patient.In the case of an acute brain condition (e.g., stroke), for instance,this can include selecting: a procedure vs. medication-only treatment(e.g., tissue plasminogen activator [tPA]), a type of procedure (e.g.,revascularization, clot retrieval, aspiration,catheter/microcatheter-based surgical intervention, meshing, stenting,aneurysm clipping, endovascular microcoil embolization, balloon-assistedcoiling, etc.), and/or selecting any other care and/or features of carefor the patient.

In variations in which a procedure (e.g., surgery) is going to beperformed, S240 can optionally additionally or alternatively includeselecting (e.g., recommending, initiating, etc.) a medical device foruse in the procedure. This preferably functions to enable early andaccurate decision making for which device(s) to use in treating thepatient, as an early choice of a proper device can improve the safetyand efficacy of the procedure, reduce time to intervention, reduce costand waste, and/or can confer any other benefits.

Selecting the medical device can include any or all of: a type ofmedical device, features (e.g., size, material composition, features,etc.) of a medical device, and/or any other information. In somevariations, for instance, S240 can include selecting any or all of: acatheter diameter (e.g., based on vessel diameter, based on smallestvessel diameter, based on vessel diameter immediately before theocclusion, etc.), a catheter length (e.g., based on path length, basedon length of one or more vessels, etc.), a catheter material (e.g.,catheter flexibility based on vessel tortuosity), a catheter type (e.g.,twist end catheter, suction catheter, etc.), whether or not aspirationis involved in the procedure (e.g., based on calcification of clot), adevice type (e.g., catheter, revascularization device, coil, braid,aspiration system, etc.), a determination of whether or not to perform aprocedure (e.g., based on a size of a clot, based on a calcification ofa clot, etc.), and/or any other features.

In specific examples, for instance, S240 can include selecting adiameter of a catheter that is as large as possible while being nolarger than a diameter of the narrowest part of the vessel needed topass through to access the clot.

In additional or alternative specific examples, S240 includes selectinga catheter based on other features of the patient's anatomy and/orpathology, such as, but not limited to: a length of the catheter (e.g.,based on a proposed path and associated path length for reaching theclot, based on a proposed access point for inserting the catheter intothe patient, etc.); a stiffness and/or flexibility of the catheter(e.g., based on a tortuosity of the vessels needed to be traversed toreach the clot); a wall thickness of the catheter (e.g., based on atortuosity of the vessels needed to be traversed to reach the clot); amaterial of the catheter; and/or any other features.

S240 can optionally additionally or alternatively include determining(e.g., predicting, recommending, etc.) features of the surgicalprocedure, such as a recommended path to reach an occlusion, an optimalentry point (e.g., groin, wrist, etc.) with which to insert a catheter,and/or any other features. In some examples, for instance, S240 caninclude warning surgeons of features that may cause delays during aprocedure, which can have significant benefits as the surgeon plans anapproach with a catheter—as such, the surgeon can more successfullyselect a device, modify a device and/or device selection based on thepath, and/or enable them to otherwise better prepare for a surgery. Inother examples, S240 can include automatically determining an optimalpath (e.g., ordered set of vessels) for the surgeon to take to reach anocclusion or other location—this can function, for instance, to enableany or all of: decreasing the time conventionally spent by the surgeonon path planning, selecting a path with minimal tortuosity and/or a pathwhich has no curvature exceeding a predetermined threshold, selecting apath with minimal narrowing and/or calcification, and/or otherwiseselecting path. In some instances, S240 can further include providing avisualization of this path, such as at a 3D visualization provided inS220 (e.g., as an annotation overlaid on the 3D visualization).

S240 can optionally additionally or alternatively include determiningwhether or not to provide medication (e.g., tPA) to the patient,selecting features (e.g., dosage, duration, etc.) of medication, and/orany other information. In specific examples, for instance, the porosityor perviousness of the clot can be used to predict the clot's responseto tPA, wherein in an event that the clot's porosity is above apredetermined threshold, tPA can be prescribed and/or administered tothe patient (e.g., with a surgical procedure, instead of a surgicalprocedure, in a smaller dosage than if the clot was less porous, etc.).

S240 can optionally additionally or alternatively include generatingand/or transmitting a message (e.g., alert, notification, etc.) to oneor more users. The message is preferably delivered at a clientapplication executing at a mobile device of the user (e.g., as describedabove), but can additionally or alternatively be delivered to astationary device (e.g., workstation) and/or any other device(s). Thiscan function, for instance, to alert a surgeon to a patient coming infor care (e.g., to help him or her prep earlier and/or in a mobilesetting such as on the way to the hospital), to inform a tech team(e.g., surgical technologists) to have a particular device (e.g.,automatically determined, determined by a surgeon, etc.) ready for aprocedure, and/or can perform any other function(s). The alert ispreferably automatically generated and sent (e.g., based on a set ofmachine learning models, based on a lookup table, etc.), but canadditionally or alternatively be manually generated or any combination.

In specific examples, for instance, the name and information associatedwith a recommended device can be messaged to any or all of: a surgeon, asurgical technologist, a medical device sales representative associatedwith the surgeon, and/or any other users.

S240 can optionally additionally or alternatively include establishingcommunication between two or more users, such as at the clientapplication and/or at any other platforms (e.g., paging system, 3rdparty messaging system, text messaging platform, etc.). This caninclude, for instance, any or all of: automatically establishing amessage thread between users, automatically calling a second user from afirst user's mobile device, automatically paging a second user from afirst user, and/or establishing communication in any other way(s). Insome examples, for instance, S240 can establish communication betweenany or all of: a surgeon and a medical device sales representative; asurgeon and a surgical technologist; all members of surgical team;and/or any other members. In a specific example shown in FIG. 3 , forinstance, S240 can include establishing communication between a surgeonand a surgical technologist to coordinate on the selection of a medicaldevice to be prepared and used in surgery.

S240 can optionally additionally or alternatively include triggering anyother actions such as, but not limited to, any or all of: the automaticordering of a medical device; the automatic assembly/assignment of asurgical team; the automatic scheduling of a surgery; the initiation ofthe transfer of the patient from a first point of care to a second pointof care (e.g., comprehensive stroke center); and/or any other actions.

In some variations, for instance, S240 can include automaticallyreferencing (e.g., checking) an inventory database associated with anyor all of: a user (e.g., specialist), the patient (e.g., inventoryassociated with the healthcare facility at which the patient iscurrently located and/or en route to), a healthcare facility (e.g.,healthcare facility at which a specialist is located, healthcarefacility at which the patient is located, healthcare facility at whichthe patient will receive treatment, etc.), and/or any other inventorydatabase. In a set of examples, referencing an inventory database canfunction to determine which devices are available for selection, suchthat an optimal device which is available based on the inventorydatabase can be recommended and/or suggested to the user. In another setof examples, additional or alternative to the first, the inventorydatabase can be referenced in response to determining an optimal device,wherein in an event that the optimal device is not present, a secondaction (e.g., recommending an alternative device and/or treatmentoption, contacting a device representative or inventory managemententity to procure the device, etc.) can be triggered.

In a first variation, S240 includes automatically recommending acatheter for the removal of a clot, wherein the particular catheter isdetermined based on a set of vessel diameters (e.g., narrowest part ofvessel; diameter immediately before the clot; diameters of vessels atthe ICA, MCA, M2, etc.) and/or any other information. S240 canadditionally include automatically messaging one or more users (e.g., atthe client application) with this recommendation and/or automaticallyplacing an order for the device.

Additionally or alternatively, S240 can include any other processes.

The method 200 can additionally or alternatively include any otherprocesses, such as, but not limited to, any or all of: training and/orre-training (e.g., updating) any or all of a set of models (e.g., basedon an outcome of a procedure performed based on an output from themethod) and/or any other processes.

5. Variations

In a first variation, the system and/or method are configured for any orall of: checking for a suspected condition (e.g., stroke, large vesselocclusion, intracerebral hemorrhage [ICH], ischemic stroke, hemorrhagicstroke, cardiac condition, pulmonary condition, trauma, etc.) based onprocessing a set of diagnostic images with a set of trained modelsand/or algorithms; identifying and optionally reconstructing (e.g., witha set of segmentation processes) a region from the set of images (e.g.,based on the set of trained models and/or algorithms); calculating a setof parameters associated with the region; optionally making adetermination that a particular condition is suspected based onanalyzing (e.g., comparing with a set of thresholds) a first subset ofthe set of parameters; based on any or all of the set of parameters(e.g., the first subset, another subset, etc.), automaticallydetermining a treatment option (e.g., based on aggregated informationfrom historical procedures performed for a corpus of patients with thatparticular condition and their associated outcomes, based on referencinga lookup table and/or database, with a trained model, etc.), where thetreatment option can include any or all of: a recommended procedure, arecommended device for a procedure, recommended features of the device,a non-surgical treatment recommendation, a recommended healthcarefacility for receiving treatment, and/or any other treatment options;optionally selecting a recipient (e.g., based on the pathologicalcondition, based on an availability associated with the recipient, basedon an availability and/or schedule associate with the recipient, basedon a location of the patient and/or the recipient, etc.); and triggeringone or more actions (e.g., notification at an application of therecipient which includes a treatment option recommendation, referencingan inventory database to check for availability of a recommended device,automatically establishing communication between a specialist and amedical device sales representative associated with a recommended deviceto decrease the time required for the specialist to obtain therecommended device, etc.) in response to determining the treatmentoption.

The method can additionally include checking for multiple potentialconditions (e.g., in parallel with checking for the neurologicalcondition, in series with checking for the neurological condition),where in response to detecting which (if any) of the potentialconditions apply, the process to determine a treatment option isperformed specifically for the that particular potential condition.

In a first set of examples, the method includes: receiving a set of(e.g., CT, CTA, etc.) images; processing the set of images with a set oftrained (e.g., machine learning, deep learning, etc.) models and/oralgorithms to segment a set of vessels from the set of images; analyzingthe segmented vessels to determine a set of diameters associated withthe segmented vessels and/or any other regions associated with theimages; comparing a portion of these set of diameters (e.g., thosecorresponding to a vessel obstruction/occlusion) with a set ofthresholds to determine if a suspected neurological condition (e.g.,large vessel occlusion, stroke, ischemic stroke, hemorrhagic stroke,etc.) is present and/or which particular neurological condition issuspected; in response to determining that a neurological condition issuspected, analyzing a second portion of diameters (e.g., different thanthe first portion, same as the first portion, overlapping with the firstportion, etc.) outside of the pathology (e.g., vessel diameterimmediately proximal to the obstruction, vessel diameter of thenarrowest vessel in an approach path to reaching the obstruction, etc.)to select a recommended device (e.g., catheter having a largest possiblediameter while being smaller than the narrowest vessel, catheter havinga largest possible diameter while being smaller than the vessel diameterimmediately proximal the obstruction, etc.) (e.g., referencing adatabase of available catheter [e.g., based on current inventory, absentof current inventory information, etc.] sizes); selecting a specialistassociated with treatment of the suspected condition and/or the patient;transmitting a notification to the specialist and optionally any otherrecipients (e.g., medical device sales representative, surgicaltechnician, inventory manager, etc.) with information regarding therecommended device (e.g., model number, features, etc.); and optionallytriggering any other actions (e.g., establishing communication betweenindividuals in response to the specialist accepting and/or overriding arecommended device, recommending a second recommended device in responseto the first recommended device not being present in local inventory,contacting a second specialist in an event that the first specialistdoes not respond within a predetermined time threshold, etc.).

In a second set of examples, additional or alternative to recommending aparticular device, the method includes automatically determining and/orrecommending other features of a treatment and/or care of the patient,such as, but not limited to: a category of devices and/or procedures; anon-surgical intervention (e.g., medication recommendation, TissuePlasminogen Activator [tPA] administration, etc.); a location at whichto intervene and/or deploy devices; a timing of any or all procedures; aspecialist for performance of the procedure; a healthcare facility atwhich a procedure is to be performed; and/or any other features.

In a third set of examples, in which a suspected aneurysm is detected,for instance, the method can include (additional or alternative to anyof the processes described above) recommending a particular type ofprocedure and/or associated device (e.g., based on a size of thesuspected aneurysm, based on a location of the suspected aneurysm, basedon a proximity of the suspected aneurysm to other vessel anatomy, etc.),such as, for instance: a coiling of a suspected aneurysm (and optionallya particular coil recommendation such as a coil packing density, coilsize, etc.) versus deploying an intrasaccular flow disruptor (e.g.,braided-wire device, WEB device, self-expanding device etc.) (e.g., upondetermining that the suspected aneurysm is located at an arterialbifurcation) (and optionally a particular intrasaccular flow disruptordevice) versus deploying a flow diverting stent (and optionally aparticular flow diverting stent [e.g., stent size, stent shape, etc.]and/or location).

In a fourth set of examples, in which a suspected clot and/or vesselocclusion is detected (e.g., based on brain images, based on cardiacimages, etc.), for instance, the method can include (additional oralternative to any of the processes described above) automaticallydetermining a recommendation of any or all of: a procedure type and/ortypes (e.g., balloon angioplasty, stent deployment, stent deploymentwith balloon angioplasty, etc.); a device type; a non-surgicalintervention (e.g., tPA); a combination of surgical and non-surgicalinterventions; a timing of treatments; and/or any other recommendations.The recommendation is preferably determined based at least on ananalysis of a set of images (e.g., CT images), such as based on any orall of: a location of the clot, the patient's vessel anatomy (e.g.,location of occlusion relative to vessel bifurcations, vessel diameters,how close the occlusion is to the patient's carotid artery, etc.),features of the clot (e.g., level of calcification, porosity, etc.),blood flow information (e.g., flow rates proximal to occlusion), and/orany other features.

In a fifth set of examples, in which a suspected subdural hematoma isdetected (e.g., based on brain images), for instance, the method caninclude (additional or alternative to any of the processes describedabove) analyzing (e.g., with a set of AI algorithms and/or models) a setof images to recommend one or more of: an endoscopic procedure to blockoff (e.g., with glue) one or more vessels contributing to the subduralhematoma; a craniotomy; a burr hole procedure; a lack of surgicalintervention (e.g., monitoring for changes); and/or any other nextsteps. In particular examples, for instance, the set of models and/oralgorithms locate and examine a particular artery and/or vasculature(e.g., middle meningeal artery), such as a vasculature involved inreducing a rebleeding rate, in order to make a recommendation.Additionally or alternatively, any or all of the following can be usedin making a recommendation: a diameter of a bleed, a volume of a bleed,whether or not (and/or to what extent) a midline shift is present,and/or any other features.

In a sixth set of examples, in which a suspected hemorrhage (e.g.,intracerebral hemorrhage [ICH]) is detected, for instance, a particularprocedure (e.g., open surgery, minimally invasive surgery, endoscopicprocedure), an associated device, a non-surgical intervention, and/orany other treatment decisions can be automatically made based on any orall of: a size of bleed, a location of a bleed, and/or any otherfeatures.

In a seventh set of examples, in which cardiac valve disease, forinstance, is suspected/detected (e.g., based on analysis of anechocardiogram), the method can (additional or alternative to any of theprocesses described above) determine whether or not to recommend theinsertion of a valve and optionally which valve (e.g., size, type,shape, location, etc.) to recommend based on any or all of: evidence ofmitral regurgitation, evidence of aortic stenosis, blood flow rates,and/or any other features.

In an eighth set of examples, in which X-ray data is analyzed, bonetrauma (e.g., fracture, breakage, etc.) can be detected and arecommendation made which informs treatment (e.g., which spinal vertebrais damaged, which devices [e.g., plates, screws, casts, etc.] should beimplemented, etc.).

In a second variation, additional or alternative to the first, non-imagedata is analyzed and used in making any or all recommendations.

In a set of examples, for instance, a set of ECG signals are analyzed(e.g., with a set of models and/or algorithms) to detect whether acardiac condition (e.g., hypertrophic cardiomyopathy [HCM]) issuspected, with treatment options and/or features (e.g., intervention,timing intervention, etc.) optionally recommended.

In a third variation (e.g., as shown in FIG. 8 ), additional oralternative to those described above, the method can include any or allof: receiving a set of data (e.g., image data, signal data, databaseinformation such as historical patient information and/or inventoryinformation and/or specialist information, etc.); processing the datawith one or more sets of models and/or algorithms, each of the sets ofmodels and/or algorithms associated with a particularpathology/condition (e.g., large vessel occlusion, intracerebralhemorrhage, subdural hematoma, etc.) and/or category ofpathologies/conditions (e.g., neural conditions, cardiac conditions,pulmonary conditions, etc.); producing a set of parameters with each ofthe set of models and/or algorithms; for each of the set of parameters,comparing a portion or all of the set of parameters with a set ofthresholds to determine if an associated condition is suspected; if acondition is suspected, further processing (e.g., with a set of modelsand/or algorithms, with a set of rules and/or lookup tables and/ordatabases, etc.) any or all of the associated set of parameters (e.g., adifferent subset of parameters than those used to detect the suspectedcondition, a same subset of parameters as those used to detect thesuspected condition, an overlapping subset with those used to detect thesuspected condition, etc.) to determine a set of recommendations (e.g.,recommended device and/or treatment for the pathology, recommendedspecialist to contact, etc.); and triggering one or more actionsassociated with the recommendation(s) (e.g., contacting a specialistwith the recommended device, checking an inventory database to check ifthe recommended device is present, establishing communication betweenusers, checking to see if a specialist accepts treatment and/or therecommendation and if the specialist does not respond within apredetermined threshold alerting a second specialist, etc.).

In a first set of examples, the sets of models and/or algorithms to beused in checking for a set of suspected conditions can be selected basedon features in the set of data, such as, but not limited to, a type ofimage data (e.g., CT vs. MRI vs. ultrasound), metadata associated withthe image data (e.g., indicating the anatomical region being imaged suchthat pathologies associated with that anatomical region are considered),historical information associated with the patient (e.g., previousconditions diagnosed for the patient, clinical notes of conditions beingmonitored, etc.), demographic information associated with the patient,type of data (e.g., ECG signal data triggers consideration of cardiacconditions), and/or any other features.

In a second set of examples, additional or alternative to the first, afirst subset of parameters associated with a pathological region (e.g.,segmentation which includes a pathology) is used to determine if asuspected condition is present, and a second subset of parametersassociated with a non-pathological region (e.g., reconstructed vesselsproximal to a pathology [e.g., occlusion]) is used to make therecommendation.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes, wherein the method processes can beperformed in any suitable order, sequentially or concurrently.

Embodiments of the system and/or method can include every combinationand permutation of the various system components and the various methodprocesses, wherein one or more instances of the method and/or processesdescribed herein can be performed asynchronously (e.g., sequentially),contemporaneously (e.g., concurrently, in parallel, etc.), or in anyother suitable order by and/or using one or more instances of thesystems, elements, and/or entities described herein. Components and/orprocesses of the following system and/or method can be used with, inaddition to, in lieu of, or otherwise integrated with all or a portionof the systems and/or methods disclosed in the applications mentionedabove, each of which are incorporated in their entirety by thisreference.

Additional or alternative embodiments implement the above methods and/orprocessing modules in non-transitory computer-readable media, storingcomputer-readable instructions. The instructions can be executed bycomputer-executable components integrated with the computer-readablemedium and/or processing system. The computer-readable medium mayinclude any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, non-transitory computer readable media, or any suitable device.The computer-executable component can include a computing system and/orprocessing system (e.g., including one or more collocated ordistributed, remote or local processors) connected to the non-transitorycomputer-readable medium, such as CPUs, GPUs, TPUS, microprocessors, orASICs, but the instructions can alternatively or additionally beexecuted by any suitable dedicated hardware device, with transitorycomputer-readable media, and/or in any other suitable ways.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for providing computer-aided decision guidance toa user, the method comprising: receiving a set of data associated with apatient, the set of data comprising a set of images produced by animaging modality located at a first point of care; processing the set ofimages with a set of trained models to check for a suspected pathologyassociated with the set of images; in response to detecting thesuspected pathology, identifying a pathological region associated withthe suspected pathology; identifying a second region separate anddistinct from the pathological region; calculating a set of parametersassociated with the second region; automatically selecting a device froma set of devices based on the set of parameters; transmitting anotification comprising information associated with the selected deviceto the user, wherein the user comprises a specialist associated with thesuspected pathology; and in response to receiving an input from thespecialist, wherein the input comprises acceptance of the selecteddevice, triggering an action associated with transporting the selecteddevice to the specialist.